Overview

Dataset statistics

Number of variables11
Number of observations6597
Missing cells5197
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory567.1 KiB
Average record size in memory88.0 B

Variable types

NUM8
CAT2
BOOL1

Warnings

Forty has 237 (3.6%) missing values Missing
Vertical has 367 (5.6%) missing values Missing
BenchReps has 1984 (30.1%) missing values Missing
BroadJump has 389 (5.9%) missing values Missing
Shuttle has 2220 (33.7%) missing values Missing
df_index has unique values Unique
Player has unique values Unique

Reproduction

Analysis started2021-01-11 06:55:22.960094
Analysis finished2021-01-11 06:55:36.512347
Duration13.55 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct6597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3412.339851
Minimum0
Maximum6822
Zeros1
Zeros (%)< 0.1%
Memory size51.5 KiB
2021-01-11T14:55:36.579212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile341.8
Q11707
median3411
Q35117
95-th percentile6480.2
Maximum6822
Range6822
Interquartile range (IQR)3410

Descriptive statistics

Standard deviation1969.642706
Coefficient of variation (CV)0.5772117642
Kurtosis-1.1991789
Mean3412.339851
Median Absolute Deviation (MAD)1705
Skewness-0.001188134182
Sum22511206
Variance3879492.388
MonotocityStrictly increasing
2021-01-11T14:55:36.719512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
33311< 0.1%
 
12581< 0.1%
 
33071< 0.1%
 
53601< 0.1%
 
12661< 0.1%
 
33151< 0.1%
 
53681< 0.1%
 
12741< 0.1%
 
33231< 0.1%
 
Other values (6587)658799.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
68221< 0.1%
 
68201< 0.1%
 
68191< 0.1%
 
68181< 0.1%
 
68171< 0.1%
 

Player
Categorical

UNIQUE

Distinct6597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
Vinnie Sunseri
 
1
Ramon Foster
 
1
Jordan Mack
 
1
Nico Johnson
 
1
Steven Johnson
 
1
Other values (6592)
6592 
ValueCountFrequency (%) 
Vinnie Sunseri1< 0.1%
 
Ramon Foster1< 0.1%
 
Jordan Mack1< 0.1%
 
Nico Johnson1< 0.1%
 
Steven Johnson1< 0.1%
 
Russell Wilson1< 0.1%
 
Simmie Cobbs1< 0.1%
 
Ryan Tujague1< 0.1%
 
Josh Freeman1< 0.1%
 
Jake Matthews1< 0.1%
 
Other values (6587)658799.8%
 
2021-01-11T14:55:36.892900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6597 ?
Unique (%)100.0%
2021-01-11T14:55:37.062481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length13
Mean length12.91450659
Min length7

Pos
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
DB
1240 
OL
1093 
DL
1068 
WR
942 
LB
777 
Other values (5)
1477 
ValueCountFrequency (%) 
DB124018.8%
 
OL109316.6%
 
DL106816.2%
 
WR94214.3%
 
LB77711.8%
 
RB5969.0%
 
QB3825.8%
 
TE3785.7%
 
FB1181.8%
 
NT3< 0.1%
 
2021-01-11T14:55:37.201449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-11T14:55:37.335125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:37.546533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Ht
Real number (ℝ≥0)

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.83810823
Minimum65
Maximum82
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:37.682167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile69
Q172
median74
Q376
95-th percentile78
Maximum82
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.622993316
Coefficient of variation (CV)0.03552357148
Kurtosis-0.4644781152
Mean73.83810823
Median Absolute Deviation (MAD)2
Skewness-0.1605866643
Sum487110
Variance6.880093934
MonotocityNot monotonic
2021-01-11T14:55:37.779005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
7594914.4%
 
7485913.0%
 
7683612.7%
 
7379912.1%
 
7273711.2%
 
776099.2%
 
715929.0%
 
704066.2%
 
782944.5%
 
692313.5%
 
Other values (8)2854.3%
 
ValueCountFrequency (%) 
651< 0.1%
 
6690.1%
 
67330.5%
 
68741.1%
 
692313.5%
 
ValueCountFrequency (%) 
822< 0.1%
 
8140.1%
 
80380.6%
 
791241.9%
 
782944.5%
 

Wt
Real number (ℝ≥0)

Distinct205
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.1881158
Minimum149
Maximum375
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:37.929605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum149
5-th percentile186
Q1206
median235
Q3283
95-th percentile320
Maximum375
Range226
Interquartile range (IQR)77

Descriptive statistics

Standard deviation45.12082282
Coefficient of variation (CV)0.1847789466
Kurtosis-0.9579274421
Mean244.1881158
Median Absolute Deviation (MAD)32
Skewness0.4840688377
Sum1610909
Variance2035.888652
MonotocityNot monotonic
2021-01-11T14:55:38.052356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
211861.3%
 
204831.3%
 
205821.2%
 
210811.2%
 
195801.2%
 
213761.2%
 
199761.2%
 
193751.1%
 
209751.1%
 
197751.1%
 
Other values (195)580888.0%
 
ValueCountFrequency (%) 
1491< 0.1%
 
1551< 0.1%
 
1561< 0.1%
 
1602< 0.1%
 
1631< 0.1%
 
ValueCountFrequency (%) 
3751< 0.1%
 
3701< 0.1%
 
3692< 0.1%
 
3661< 0.1%
 
3641< 0.1%
 

Forty
Real number (ℝ≥0)

MISSING

Distinct159
Distinct (%)2.5%
Missing237
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean4.775927673
Minimum4.22
Maximum6.05
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:38.184119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.22
5-th percentile4.4
Q14.54
median4.69
Q34.97
95-th percentile5.36
Maximum6.05
Range1.83
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.3054292303
Coefficient of variation (CV)0.06395181235
Kurtosis-0.1377582138
Mean4.775927673
Median Absolute Deviation (MAD)0.19
Skewness0.8004250999
Sum30374.9
Variance0.09328701474
MonotocityNot monotonic
2021-01-11T14:55:38.323748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.51492.3%
 
4.561362.1%
 
4.651352.0%
 
4.621302.0%
 
4.581241.9%
 
4.521221.8%
 
4.591221.8%
 
4.531211.8%
 
4.61121.7%
 
4.511071.6%
 
Other values (149)510277.3%
 
(Missing)2373.6%
 
ValueCountFrequency (%) 
4.222< 0.1%
 
4.241< 0.1%
 
4.261< 0.1%
 
4.273< 0.1%
 
4.2850.1%
 
ValueCountFrequency (%) 
6.051< 0.1%
 
61< 0.1%
 
5.991< 0.1%
 
5.861< 0.1%
 
5.852< 0.1%
 

Vertical
Real number (ℝ≥0)

MISSING

Distinct165
Distinct (%)2.6%
Missing367
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean33.01140864
Minimum17.5
Maximum46
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:38.459852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile26
Q130.5
median33.5
Q335.61517887
95-th percentile39
Maximum46
Range28.5
Interquartile range (IQR)5.115178868

Descriptive statistics

Standard deviation3.980787058
Coefficient of variation (CV)0.1205882215
Kurtosis0.04981076979
Mean33.01140864
Median Absolute Deviation (MAD)2.5
Skewness-0.284430932
Sum205661.0759
Variance15.8466656
MonotocityNot monotonic
2021-01-11T14:55:38.576542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
332744.2%
 
342573.9%
 
33.52553.9%
 
35.52473.7%
 
352393.6%
 
34.52363.6%
 
362333.5%
 
32.52313.5%
 
322203.3%
 
311932.9%
 
Other values (155)384558.3%
 
(Missing)3675.6%
 
ValueCountFrequency (%) 
17.51< 0.1%
 
191< 0.1%
 
19.53< 0.1%
 
201< 0.1%
 
20.570.1%
 
ValueCountFrequency (%) 
461< 0.1%
 
45.51< 0.1%
 
4540.1%
 
44.52< 0.1%
 
443< 0.1%
 

BenchReps
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)1.0%
Missing1984
Missing (%)30.1%
Infinite0
Infinite (%)0.0%
Mean20.84825493
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:39.429089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q116
median21
Q325
95-th percentile32
Maximum49
Range47
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.357309962
Coefficient of variation (CV)0.3049324743
Kurtosis0.09351553067
Mean20.84825493
Median Absolute Deviation (MAD)4
Skewness0.2618927776
Sum96173
Variance40.41538995
MonotocityNot monotonic
2021-01-11T14:55:39.564160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
192874.4%
 
212824.3%
 
232764.2%
 
202664.0%
 
242654.0%
 
222654.0%
 
172563.9%
 
182533.8%
 
152433.7%
 
162203.3%
 
Other values (35)200030.3%
 
(Missing)198430.1%
 
ValueCountFrequency (%) 
21< 0.1%
 
32< 0.1%
 
450.1%
 
550.1%
 
6100.2%
 
ValueCountFrequency (%) 
491< 0.1%
 
453< 0.1%
 
4440.1%
 
431< 0.1%
 
4240.1%
 

BroadJump
Real number (ℝ≥0)

MISSING

Distinct168
Distinct (%)2.7%
Missing389
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean114.6500863
Minimum74
Maximum147
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:39.713805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile98
Q1109
median116
Q3121
95-th percentile127
Maximum147
Range73
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.860597165
Coefficient of variation (CV)0.07728382467
Kurtosis0.2181862518
Mean114.6500863
Median Absolute Deviation (MAD)5.395280889
Skewness-0.5001502948
Sum711747.7355
Variance78.51018212
MonotocityNot monotonic
2021-01-11T14:55:39.848480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1202764.2%
 
1182523.8%
 
1152343.5%
 
1172333.5%
 
1162333.5%
 
1212323.5%
 
1132103.2%
 
1192093.2%
 
1141953.0%
 
1221912.9%
 
Other values (158)394359.8%
 
(Missing)3895.9%
 
ValueCountFrequency (%) 
741< 0.1%
 
781< 0.1%
 
822< 0.1%
 
841< 0.1%
 
8540.1%
 
ValueCountFrequency (%) 
1471< 0.1%
 
1413< 0.1%
 
1401< 0.1%
 
1393< 0.1%
 
1383< 0.1%
 

Shuttle
Real number (ℝ≥0)

MISSING

Distinct235
Distinct (%)5.4%
Missing2220
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean4.495179347
Minimum3.73
Maximum8.28
Zeros0
Zeros (%)0.0%
Memory size51.5 KiB
2021-01-11T14:55:39.997081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile4.03
Q14.2
median4.36
Q34.59
95-th percentile5.052
Maximum8.28
Range4.55
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.5942651132
Coefficient of variation (CV)0.1322005347
Kurtosis14.97982115
Mean4.495179347
Median Absolute Deviation (MAD)0.18
Skewness3.638234266
Sum19675.4
Variance0.3531510247
MonotocityNot monotonic
2021-01-11T14:55:40.139669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.41051.6%
 
4.281011.5%
 
4.21941.4%
 
4.2921.4%
 
4.15841.3%
 
4.25841.3%
 
4.18821.2%
 
4.32811.2%
 
4.07771.2%
 
4.33721.1%
 
Other values (225)350553.1%
 
(Missing)222033.7%
 
ValueCountFrequency (%) 
3.731< 0.1%
 
3.751< 0.1%
 
3.781< 0.1%
 
3.81< 0.1%
 
3.812< 0.1%
 
ValueCountFrequency (%) 
8.281< 0.1%
 
8.151< 0.1%
 
8.131< 0.1%
 
8.061< 0.1%
 
8.021< 0.1%
 

pro bowl?
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.5 KiB
0
6046 
1
 
551
ValueCountFrequency (%) 
0604691.6%
 
15518.4%
 
2021-01-11T14:55:40.235411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-01-11T14:55:26.717997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:26.863606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:26.994290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.127932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.284481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.462052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.634621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.768293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:27.894960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.043563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.184248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.316926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.468514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.604700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.724481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:28.867100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.006727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.181259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.319889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.463504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.607120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.740304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:29.875972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.019587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.144222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.273907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.410548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.547178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.723704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.845380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:30.975644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.101310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.239939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.386577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.511211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.645851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.784516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:31.916178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.049349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.187975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.303676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.434357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.568002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.697654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.826314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:32.933025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.044725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.166401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.286081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.423705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.557356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.699973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.839571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:33.982218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.111339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.248969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.379621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.511268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.646209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:34.776308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:35.016616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:35.158240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:35.300855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:35.436527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-11T14:55:40.322187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-11T14:55:40.553772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-11T14:55:40.774185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-11T14:55:40.975834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-11T14:55:35.645966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:36.021791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:36.207165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-11T14:55:36.404635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexPlayerPosHtWtFortyVerticalBenchRepsBroadJumpShuttlepro bowl?
00John AbrahamLB762524.5535.190945NaN119.927667NaN1
11Shaun AlexanderRB722184.5834.872769NaN119.171752NaN1
22Darnell AlfordOL763345.5625.00000023.094.0000004.980
33Kyle AllamonTE742534.9729.000000NaN104.0000004.490
44Rashard AndersonDB742064.5534.000000NaN123.0000004.150
56LaVar ArringtonLB752504.5335.403062NaN120.431610NaN1
67Corey AtkinsLB722374.7231.00000021.0112.0000004.390
79Reggie AustinDB691754.4435.00000017.0119.0000004.140
811Mark BaniewiczOL783125.3428.00000020.096.0000004.730
912Rashidi BarnesDB722084.6235.00000010.0114.0000004.320

Last rows

df_indexPlayerPosHtWtFortyVerticalBenchRepsBroadJumpShuttlepro bowl?
65876812Logan WilsonLB742414.6332.021.0121.04.270
65886813Rob WindsorDL762854.9028.521.0111.04.440
65896814Antoine WinfieldDB702054.4536.0NaN124.0NaN1
65906815Tristan WirfsOL773224.8536.524.0121.04.680
65916816Steven WirtelDB762274.7626.0NaN120.04.280
65926817Charlie WoernerTE772454.7834.521.0120.04.460
65936818D.J. WonnumDL772544.7334.520.0123.04.440
65946819Dom Wood-AndersonTE762574.9235.0NaN119.0NaN0
65956820David WoodwardLB742354.7933.516.0114.04.370
65966822Jabari ZunigaDL752534.6433.029.0127.0NaN0